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Recent Heavy Flavor Results in ATLAS
No full textStudying heavy-flavour hadron properties provides a extensive tests for various QCD predictions as well as a means to probe the Standard Model validity. ATLAS experiment, being a general-purpose detector at LHC, is particularly successful in such measurements with final states involving muons, thanks to large collected integrated luminosity and precise muon reconstruction and triggering. This talk will overview the recent ATLAS results on heavy-flavour hadron production and decay properties and spectroscopy of exotic states
Multiplicity dependence of production in pp collisions at 13 TeV
No full textThe dependence of (980) production on the final-state charged-particle multiplicity is reported for proton--proton (pp) collisions at the centre-of-mass energy, 13 TeV. The production of (980) is measured with the ALICE detector via the decay channel in a midrapidity region of ~0.5. The evolution of the integrated yields and mean transverse momentum of f(980) as a function of charged-particle multiplicity measured in pp at 13 TeV follows the trends observed in pp at 5.02 TeV and in proton--lead (p--Pb) collisions at 5.02 TeV. Particle yield ratios of (980) to and (892) are found to decrease with increasing charged-particle multiplicity. These particle ratios are compared with calculations from the canonical statistical thermal model as a function of charged-particle multiplicity. The thermal model calculations provide a better description of the decreasing trend of particle ratios when no strange or antistrange quark composition for f(980) is assumed, which suggests that the tetraquark interpretation of the f(980) is disfavored.The dependence of f(980) production on the final-state charged-particle multiplicity is reported for proton-proton (pp) collisions at the centre-of-mass energy, TeV. The production of f(980) is measured with the ALICE detector via the f decay channel in a midrapidity region of . The evolution of the integrated yields and mean transverse momentum of f(980) as a function of charged-particle multiplicity measured in pp at TeV follows the trends observed in pp at TeV and in proton-lead (p-Pb) collisions at TeV. Particle yield ratios of f(980) to and K(892) are found to decrease with increasing charged-particle multiplicity. These particle ratios are compared with calculations from the canonical statistical thermal model as a function of charged-particle multiplicity. The thermal model calculations provide a better description of the decreasing trend of particle ratios when no strange or antistrange quark composition for f(980) is assumed, which suggests that the tetraquark interpretation of the f(980) is disfavored
Point Cloud Machine Learning for Cell-to-Track Association: Enhancing Event Reconstruction in High Energy Physics
No full textThe ATLAS detector at CERN’s Large Hadron Collider (LHC) is a complex system composed of multiple subdetectors, each designed to capture complementary aspects of particle interactions. Thus, accurate understanding of the physical phenomena under study requires effectively combining information from these components. This work focuses on the key challenge of associating data from the inner tracker with the corresponding energy deposits in the calorimeter. Current approaches tackle this problem in a modular fashion. First, hits in the tracker and calorimeter are reconstructed separately into tracks and topo-clusters, respectively. Second, tracks are iteratively associated with topo-clusters to improve particle identification and energy calibration in subsequent reconstruction steps. However, this strategy relies on rigid algorithms tuned to address reasonably well the most common known scenarios. Moreover, they fail to fully exploit the complementary information provided by the two subsystems. Consequently, errors in track and topo-cluster reconstruction are propagated to later stages, hampering accurate association. To overcome these limitations, we propose a PointNet model for cell-to-track association. This approach enables the direct integration of tracking information into energy reconstruction, facilitates fine-grained association between tracks and individual calorimeter cells rather than whole topo-clusters, and leverages efficient point cloud data representations. This methodology demonstrates promising results for enhancing offline reconstruction, particularly relevant for addressing the increased detector occupancy and event complexity anticipated in the High-Luminosity LHC era
